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1 – 10 of over 25000Bing Xu and Junzo Watada
The study aims to reinvestigate the regional urbanization gap in China and some projects are to be presented for reducing the gap.
Abstract
Purpose
The study aims to reinvestigate the regional urbanization gap in China and some projects are to be presented for reducing the gap.
Design/methodology/approach
An innovative weighted kernel density approach is applied for identifying the regional urbanization development with population migration and investment and evaluating the projects.
Findings
The regional urbanization gap is 7 and 9 percent with the unconditional estimation, 13 (23) percent and 13 (23) percent with population (investment) weighted estimation between eastern and middle region, eastern and western region, respectively. The project on the interior migration of population by 30 percent and the project on the selective investment enhancement by 30 percent both reduce the regional urbanization gap by about 4 percent between middle and eastern region.
Research limitations/implications
Focus is only on the investigation of urbanization development level with single population migration or investment enhancement; the identifications and projects with joint impact of population and investment are not considered.
Originality/value
The study not only measures the urbanization development with the nonparametric approach but also designs some practicable projects for reducing the regional urbanization gap, which is helpful for the Chinese Government in the policymaking process.
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Joseph F. Hair, Pratyush N. Sharma, Marko Sarstedt, Christian M. Ringle and Benjamin D. Liengaard
The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis…
Abstract
Purpose
The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).
Design/methodology/approach
The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.
Findings
The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.
Research limitations/implications
In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.
Practical implications
The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.
Originality/value
To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.
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Chuanhong Miao, Xican Li and Jiehui Lu
The purpose of this paper is to establish the grey relational estimating model of soil pH value based on hyper-spectral data.
Abstract
Purpose
The purpose of this paper is to establish the grey relational estimating model of soil pH value based on hyper-spectral data.
Design/methodology/approach
As to the uncertainty of the factors affecting the soil pH value estimation based on hyper-spectral, the grey weighted relation estimation model was set up according to the grey system theory. Then the linear regression correction model is established according to the difference and grey relation degree information between the estimated samples and their corresponding pattern. At the same time, the model was applied to Hengshan county of Shanxi province.
Findings
The results are convincing: not only that the linear regression correction model of grey relation estimating pattern of soil pH value based on hyper-spectral data is valid, but also the model’s estimating accuracy is higher, which the corrected average relative error is 0.2578 per cent, and the decision coefficient R2=0.9876.
Practical implications
The method proposed in the paper can be used at soil pH value hyper-spectral inversion and even for other similar forecast problem.
Originality/value
The paper succeeds in realising both the soil pH value hyper-spectral grey relation estimating pattern based on the grey relational theory and the correction model of the estimating pattern by using the linear regression.
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Massimo Guidolin and Carrie Fangzhou Na
We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence…
Abstract
We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.
Lei Zhang, Fengchun Tian, Xiongwei Peng, Xin Yin, Guorui Li and Lijun Dang
The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide…
Abstract
Purpose
The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.
Design/methodology/approach
In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.
Findings
The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.
Originality/value
In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.
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Wei Zhang, Xianghong Hua, Kegen Yu, Weining Qiu, Xin Chang, Bang Wu and Xijiang Chen
Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve…
Abstract
Purpose
Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.
Design/methodology/approach
The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.
Findings
Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.
Research limitations/implications
Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.
Practical implications
The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.
Social implications
The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.
Originality/value
A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.
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Francesco Bravo, Kim P. Huynh and David T. Jacho-Chávez
This chapter proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the…
Abstract
This chapter proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the responses with an appropriately weighted version and then use local polynomial estimation for the average derivatives. The resulting estimator is shown to be asymptotically normal, and an estimator of its asymptotic variance–covariance matrix is also shown to be consistent. Monte Carlo experiments show that the proposed estimator has desirable finite sample properties.
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Rani Kumari, Chandrakant Lodhi, Yogesh Mani Tripathi and Rajesh Kumar Sinha
Inferences for multicomponent reliability is derived for a family of inverted exponentiated densities having common scale and different shape parameters.
Abstract
Purpose
Inferences for multicomponent reliability is derived for a family of inverted exponentiated densities having common scale and different shape parameters.
Design/methodology/approach
Different estimates for multicomponent reliability is derived from frequentist viewpoint. Two bootstrap confidence intervals of this parametric function are also constructed.
Findings
Form a Monte-Carlo simulation study, the authors find that estimates obtained from maximum product spacing and Right-tail Anderson–Darling procedures provide better point and interval estimates of the reliability. Also the maximum likelihood estimate competes good with these estimates.
Originality/value
In literature several distributions are introduced and studied in lifetime analysis. Among others, exponentiated distributions have found wide applications in such studies. In this regard the authors obtain various frequentist estimates for the multicomponent reliability by considering inverted exponentiated distributions.
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A Bayesian argument is used to suggest modifications to the Robertson/Sparck Jones relevance weighting formula, to accommodate the addition to the query of terms taken from the…
Abstract
A Bayesian argument is used to suggest modifications to the Robertson/Sparck Jones relevance weighting formula, to accommodate the addition to the query of terms taken from the relevant documents identified during the search.
Empirical evidence of education spillovers in developing countries and rural contexts is scarce and focuses on specific channels. The purpose of this paper is to provide evidence…
Abstract
Purpose
Empirical evidence of education spillovers in developing countries and rural contexts is scarce and focuses on specific channels. The purpose of this paper is to provide evidence of such spillovers in rural India, by evaluating the overall impact of neighbours' education on farm productivity.
Design/methodology/approach
The author uses cross‐sectional data from the India Human Development Survey of 2005. Spatial econometric tools are used to take into account social distance between neighbours. To be sure that the author's definition of a neighbourhood does not drive the results, three different definitions of neighbours were tested.
Findings
The results show that education spillovers are substantial: one additional year in the mean level of education of neighbours increases households' farm productivity by 2 per cent. These findings are robust to changes in specification.
Research limitations/implications
The results open the way to further research. In particular, this paper does not explore the channels through which this spillover effect happens.
Practical implications
This paper confirms the choice of improving education in developing countries: giving a child education will certainly provide him/her with greater revenues and it may also provide his/her neighbours with greater revenues. The paper shows the importance for policy makers of taking into account education spillovers and policies' complementarity when facing political trade‐offs.
Originality/value
This paper is one of the few to underline that education externalities do not only exist in urban contexts and education spillovers do not only occur between workers of the manufacturing and service sectors. There are also spillovers in sectors considered as more traditional, such as agriculture.
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